FACE_Analysis_Manual
1. Load all libraries required
library(readxl)
library(tidyverse)
library('dplyr')
library('readxl')
library(ggplot2)
library(dplyr) # required by custom function outlier_removal()
library(jmv) # ancova()
library(ggplot2) # ggplot()
library(gridExtra) # grid.arrange()2. Pre analysis: Data import and cleaning
manual_FACE<- read_excel('FACE_Thesis_Manual.xlsx')
manual_FACE <- data.frame(manual_FACE)
colnames(manual_FACE) [1] "CSF" "Diag_CSF" "Analysis_ID" "Diag_CSF_1_MCI" "Sex_1m_2f"
[6] "Age_LP" "BMI" "APOE_Status" "A_1pos_0neg" "T_1pos_0neg"
[11] "N_1pos_0neg" "AT" "AN" "YKL40_ng_mL" "sAxl_ng_mL"
[16] "Tyro3_pg_mL" "CRP_pg_mL" "sICAM1_ng_mL" "sVCAM1_ng_mL" "sTREM2_pg_mL"
[21] "C1q_ng_mL" "C3_ng_mL" "C4_ng_mL" "Factor.B_ng_mL" "Factor.H_ng_mL"
[26] "MIF_pg_mL" "TNFR1_ng_mL" "TNFR2_ng_mL" FC_thesis_manual <- subset(manual_FACE, select = c(CSF,
YKL40_ng_mL, sAxl_ng_mL, Tyro3_pg_mL,
sTREM2_pg_mL, C1q_ng_mL, C3_ng_mL, C4_ng_mL,
Factor.B_ng_mL, Factor.H_ng_mL, MIF_pg_mL,
TNFR1_ng_mL, TNFR2_ng_mL, sICAM1_ng_mL, sVCAM1_ng_mL,
CRP_pg_mL, Age_LP, Sex_1m_2f, BMI, APOE_Status,
Diag_CSF, A_1pos_0neg, T_1pos_0neg, N_1pos_0neg, AT, AN))
FC_thesis_manual$A_N_cat <- factor(ifelse(FC_thesis_manual$AN == 0,
"A-T-",
ifelse(FC_thesis_manual$AN == 2,
"A+T-",
ifelse(FC_thesis_manual$AN == 1,
"A-T+",
"A+T+"))),
levels = c("A-T-", "A-T+", "A+T-", "A+T+"))
table(FC_thesis_manual$A_N_cat)
A-T- A-T+ A+T- A+T+
314 112 152 204 FC_thesis_manual$Diag_CSF <- factor(FC_thesis_manual$Diag_CSF,
levels = c("SCD", "MCI"))
table(FC_thesis_manual$A_N_cat)
A-T- A-T+ A+T- A+T+
314 112 152 204 table(FC_thesis_manual$Diag_CSF)
SCD MCI
59 723 which(FC_thesis_manual$Diag_CSF == "SCD") [1] 50 58 63 68 177 183 184 196 202 210 219 246 356 382 387 388 391 392 393 404 409 465
[23] 484 498 499 520 587 630 632 633 635 636 640 641 644 648 649 652 655 657 660 662 663 669
[45] 670 672 675 676 679 681 683 685 686 687 688 689 765 766 767which(FC_thesis_manual$Diag_CSF == "SCD" &
FC_thesis_manual$N_1pos_0neg == 0) [1] 50 58 63 68 177 183 184 196 202 210 219 246 356 387 388 391 392 393 404 409 465 484
[23] 498 499 520 587 630 633 635 636 641 644 648 649 652 655 657 660 662 669 670 672 675 676
[45] 683 686 687 688 689 765 766 767which(FC_thesis_manual$Diag_CSF == "SCD" &
FC_thesis_manual$N_1pos_0neg == 1)[1] 382 632 640 663 679 681 685FC_thesis_manual$clinical_N_cat <- factor(ifelse(FC_thesis_manual$Diag_CSF == "SCD" &
FC_thesis_manual$N_1pos_0neg == 0,
"SCD_T-",
ifelse(FC_thesis_manual$Diag_CSF == "SCD" &
FC_thesis_manual$N_1pos_0neg == 1,
"SCD_T+",
ifelse(FC_thesis_manual$Diag_CSF == "MCI" &
FC_thesis_manual$N_1pos_0neg == 0,
"MCI_T-",
ifelse(FC_thesis_manual$Diag_CSF == "MCI" &
FC_thesis_manual$N_1pos_0neg == 1,
"MCI_T+",'others')))),
levels = c('SCD_T-', 'SCD_T+', "MCI_T-", "MCI_T+"))
table(FC_thesis_manual$clinical_N_cat)
SCD_T- SCD_T+ MCI_T- MCI_T+
52 7 414 309 Outlier removal function Higher than or less than 3 standard deviations
outlier_removal <- function(df) {
find.outlier <- function(df) {
if(!is.numeric(df)) {
df
}
else {
arith.mean <- mean(df, na.rm = TRUE)
st.dev <- sd(df, na.rm = TRUE)
df[which(df > (arith.mean + 3*st.dev) | df < (arith.mean - 3*st.dev))] <- NA
df
}
}
df %>% dplyr::mutate_all(find.outlier)
}Removed outliers
FC_thesis_manual_clean <- outlier_removal(FC_thesis_manual[1:16])Check for merge outlier removed columns
dim(FC_thesis_manual_clean)[1] 782 16FC_thesis_m <- merge(FC_thesis_manual_clean,
FC_thesis_manual[c(1, 17:ncol(FC_thesis_manual))],
by = "CSF")
dim(FC_thesis_m)[1] 782 28Did not Omit NAs
table(FC_thesis_m$A_N_cat)
A-T- A-T+ A+T- A+T+
314 112 152 204 table(FC_thesis_m$Diag)
SCD MCI
59 723 table(FC_thesis_m$clinical_N_cat)
SCD_T- SCD_T+ MCI_T- MCI_T+
52 7 414 309 colnames(FC_thesis_m) [1] "CSF" "YKL40_ng_mL" "sAxl_ng_mL" "Tyro3_pg_mL" "sTREM2_pg_mL"
[6] "C1q_ng_mL" "C3_ng_mL" "C4_ng_mL" "Factor.B_ng_mL" "Factor.H_ng_mL"
[11] "MIF_pg_mL" "TNFR1_ng_mL" "TNFR2_ng_mL" "sICAM1_ng_mL" "sVCAM1_ng_mL"
[16] "CRP_pg_mL" "Age_LP" "Sex_1m_2f" "BMI" "APOE_Status"
[21] "Diag_CSF" "A_1pos_0neg" "T_1pos_0neg" "N_1pos_0neg" "AT"
[26] "AN" "A_N_cat" "clinical_N_cat"FC_thesis_m$BMI <- as.numeric(as.character(FC_thesis_m$BMI))
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Age_LP'] <- 'Age'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Sex_1m_2f'] <- 'sex'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'BMI'] <- 'bmi'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sAxl_ng_mL'] <- 'AXL_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sTREM2_pg_mL'] <- 'TREM2_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Factor.B_ng_mL'] <- 'Factor_B_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Factor.H_ng_mL'] <- 'Factor_H_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sICAM1_ng_mL'] <- 'ICAM1_ng_mL'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sVCAM1_ng_mL'] <- 'VCAM1_ng_mL'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'YKL40_ng_mL'] <- 'YKL40_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C1q_ng_mL'] <- 'C1q_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C3_ng_mL'] <- 'C3_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C4_ng_mL'] <- 'C4_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'MIF_pg_mL'] <- 'MIF_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'TNFR1_ng_mL'] <- 'TNFR1_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'TNFR2_ng_mL'] <- 'TNFR2_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'CRP_pg_mL'] <- 'CRP_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Tyro3_pg_mL'] <- 'Tyro3_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'APOE_Status'] <- 'E4_Positive'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Diag_CSF'] <- 'Diag'
colnames(FC_thesis_m) [1] "CSF" "YKL40_ng_ml" "AXL_ng_ml" "Tyro3_pg_ml" "TREM2_pg_ml"
[6] "C1q_ng_ml" "C3_ng_ml" "C4_ng_ml" "Factor_B_ng_ml" "Factor_H_ng_ml"
[11] "MIF_pg_ml" "TNFR1_ng_ml" "TNFR2_ng_ml" "ICAM1_ng_mL" "VCAM1_ng_mL"
[16] "CRP_pg_ml" "Age" "sex" "bmi" "E4_Positive"
[21] "Diag" "A_1pos_0neg" "T_1pos_0neg" "N_1pos_0neg" "AT"
[26] "AN" "A_N_cat" "clinical_N_cat"Data is ready for analysis
3. AT scheme
3.1. YKL40_AT
(YKL40_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = YKL40_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "YKL40 (ng/mL)") +
scale_y_continuous(breaks = seq(0,1200, by = 200), limits = c(0,1200)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = YKL40_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = YKL40_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - YKL40_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
A_N_cat 698977.16500 3 232992.38833 35.425648399 < .0000001
Age 474001.76539 1 474001.76539 72.070250884 < .0000001
sex 4213.82493 1 4213.82493 0.640696811 0.4238036
bmi 19.76457 1 19.76457 0.003005131 0.9563025
E4_Positive 39.62297 1 39.62297 0.006024529 0.9381602
Residuals 3623894.31875 551 6576.94069
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -87.02058 10.969550 551.0000 -7.932922 < .0000001
- A+T- 16.05548 10.718645 551.0000 1.497903 0.8083873
- A+T+ -61.86192 9.996186 551.0000 -6.188553 < .0000001
A-T+ - A+T- 103.07607 12.674970 551.0000 8.132253 < .0000001
- A+T+ 25.15866 11.574154 551.0000 2.173694 0.1809193
A+T- - A+T+ -77.91741 11.204260 551.0000 -6.954266 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))NA3.2. AXL_AT
(AXL_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = AXL_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "AXL (ng/mL)") +
scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 6 rows containing missing values (`geom_point()`).
ancova(formula = AXL_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = AXL_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - AXL_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
A_N_cat 3090.8590183 3 1030.2863394 42.19276784 < .0000001
Age 3.8008723 1 3.8008723 0.15565510 0.6933408
sex 31.2567628 1 31.2567628 1.28004156 0.2583817
bmi 23.6264745 1 23.6264745 0.96756243 0.3257190
E4_Positive 0.5278767 1 0.5278767 0.02161785 0.8831618
Residuals 13503.4598322 553 24.4185530
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -5.742173 0.6695329 553.0000 -8.576386 < .0000001
- A+T- 2.573490 0.6531054 553.0000 3.940390 0.0005506
- A+T+ -1.712757 0.6088044 553.0000 -2.813313 0.0304666
A-T+ - A+T- 8.315663 0.7706059 553.0000 10.791071 < .0000001
- A+T+ 4.029415 0.6999810 553.0000 5.756464 < .0000001
A+T- - A+T+ -4.286248 0.6820812 553.0000 -6.284072 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))NA3.3. Tyro3_AT
(Tyro3_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = Tyro3_pg_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Tyro3 (pg/mL)") +
scale_y_continuous(breaks = seq(0,12000, by = 2000), limits = c(0,12000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = Tyro3_pg_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Tyro3_pg_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Tyro3_pg_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 1.282562e+8 3 4.275207e+7 53.9922980 < .0000001
Age 1127977.2 1 1127977.2 1.4245409 0.2331701
sex 111055.0 1 111055.0 0.1402532 0.7081733
bmi 263468.3 1 263468.3 0.3327384 0.5642862
E4_Positive 960565.2 1 960565.2 1.2131136 0.2711964
Residuals 4.370835e+8 552 791818.0
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -1104.2657 119.2019 552.0000 -9.263827 < .0000001
- A+T- 562.3025 117.6865 552.0000 4.777970 0.0000136
- A+T+ -423.4040 109.8205 552.0000 -3.855417 0.0007748
A-T+ - A+T- 1666.5682 138.0422 552.0000 12.072889 < .0000001
- A+T+ 680.8617 125.5639 552.0000 5.422433 0.0000005
A+T- - A+T+ -985.7065 123.1296 552.0000 -8.005437 < .0000001
───────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))3.4. TREM2_AT
(TREM2_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = TREM2_pg_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TREM2 (pg/mL)") +
scale_y_continuous(breaks = seq(0,14000, by = 2000), limits = c(0,14000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = TREM2_pg_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TREM2_pg_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TREM2_pg_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 1.535695e+8 3 5.118984e+7 18.8146773 < .0000001
Age 6.920858e+7 1 6.920858e+7 25.4374117 0.0000006
sex 1928794.0 1 1928794.0 0.7089226 0.4001657
bmi 8105178.7 1 8105178.7 2.9790347 0.0849081
E4_Positive 711952.1 1 711952.1 0.2616759 0.6091753
Residuals 1.504569e+9 553 2720740.0
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -1184.7196 221.9542 553.0000 -5.337676 0.0000008
- A+T- 680.9875 217.9379 553.0000 3.124686 0.0112397
- A+T+ -358.1625 203.4856 553.0000 -1.760137 0.4736245
A-T+ - A+T- 1865.7072 256.1965 553.0000 7.282330 < .0000001
- A+T+ 826.5571 233.0834 553.0000 3.546186 0.0025440
A+T- - A+T+ -1039.1501 228.0561 553.0000 -4.556555 0.0000384
───────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))3.5. C1q_AT
(C1q_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = C1q_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C1q (ng/mL)") +
scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C1q_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C1q_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C1q_ng_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 292113.686 3 97371.229 30.9764403 < .0000001
Age 122688.844 1 122688.844 39.0306633 < .0000001
sex 88145.268 1 88145.268 28.0414107 0.0000002
bmi 9132.626 1 9132.626 2.9053372 0.0888515
E4_Positive 1107.666 1 1107.666 0.3523787 0.5530137
Residuals 1728867.980 550 3143.396
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -60.29980 7.632358 550.0000 -7.900547 < .0000001
- A+T- 19.00204 7.416081 550.0000 2.562275 0.0639844
- A+T+ -18.09463 6.936984 550.0000 -2.608429 0.0560591
A-T+ - A+T- 79.30184 8.770084 550.0000 9.042313 < .0000001
- A+T+ 42.20517 7.981312 550.0000 5.287999 0.0000011
A+T- - A+T+ -37.09667 7.749536 550.0000 -4.786954 0.0000131
───────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))3.6. C3_AT
(C3_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = C3_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C3 (ng/mL)") +
scale_y_continuous(breaks = seq(0,20000, by = 5000), limits = c(0, 20000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C3_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C3_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C3_ng_ml
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
A_N_cat 9.178480e+7 3 3.059493e+7 2.7914900 0.0398646
Age 1.517606e+8 1 1.517606e+8 13.8466735 0.0002188
sex 1.436253e+8 1 1.436253e+8 13.1044087 0.0003219
bmi 1.282461e+7 1 1.282461e+7 1.1701213 0.2798521
E4_Positive 8737387 1 8737387 0.7972015 0.3723233
Residuals 6.006120e+9 548 1.096007e+7
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
─────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -1126.53868 445.0240 548.0000 -2.53141094 0.0698335
- A+T- -480.69268 440.4439 548.0000 -1.09138238 1.0000000
- A+T+ 33.89205 408.6471 548.0000 0.08293721 1.0000000
A-T+ - A+T- 645.84600 515.9204 548.0000 1.25183255 1.0000000
- A+T+ 1160.43073 468.5420 548.0000 2.47668434 0.0813697
A+T- - A+T+ 514.58473 459.2955 548.0000 1.12037843 1.0000000
─────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))3.7. C4_AT
(C4_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = C4_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C4 (ng/mL)") +
scale_y_continuous(breaks = seq(0,5000, by = 1000), limits = c(0, 5000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 15 rows containing missing values (`geom_point()`).
ancova(formula = C4_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C4_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C4_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
A_N_cat 5194540.5474 3 1731513.5158 7.737966 0.0000455
Age 2614395.7624 1 2614395.7624 11.683482 0.0006778
sex 7586443.8770 1 7586443.8770 33.903083 < .0000001
bmi 2263547.0864 1 2263547.0864 10.115573 0.0015540
E4_Positive 169.2551 1 169.2551 7.563848e-4 0.9780691
Residuals 1.217301e+8 544 223768.5521
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -261.12321 64.22089 544.0000 -4.066016 0.0003293
- A+T- 70.65175 62.98037 544.0000 1.121806 1.0000000
- A+T+ -70.35977 58.77583 544.0000 -1.197087 1.0000000
A-T+ - A+T- 331.77496 74.24166 544.0000 4.468851 0.0000574
- A+T+ 190.76344 67.65704 544.0000 2.819565 0.0299050
A+T- - A+T+ -141.01152 65.88313 544.0000 -2.140328 0.1966336
───────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))3.8. FB_AT
(FB_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = Factor_B_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor B (ng/mL)") +
scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 14 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 14 rows containing missing values (`geom_point()`).
ancova(formula = Factor_B_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_B_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_B_ng_ml
─────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────
A_N_cat 417718.4 3 139239.47 2.832831 0.0377293
Age 160875.0 1 160874.98 3.273006 0.0709783
sex 1121152.5 1 1121152.51 22.809877 0.0000023
bmi 644795.9 1 644795.89 13.118389 0.0003197
E4_Positive 109662.2 1 109662.16 2.231080 0.1358370
Residuals 2.683703e+7 546 49152.06
─────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -73.93245 29.90412 546.0000 -2.4723167 0.0823660
- A+T- 19.61140 29.52044 546.0000 0.6643330 1.0000000
- A+T+ -15.63960 27.37352 546.0000 -0.5713403 1.0000000
A-T+ - A+T- 93.54385 34.58567 546.0000 2.7046998 0.0423001
- A+T+ 58.29285 31.20633 546.0000 1.8679813 0.3738006
A+T- - A+T+ -35.25100 30.76183 546.0000 -1.1459334 1.0000000
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))3.9. FH_AT
(FH_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = Factor_H_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor H (ng/mL)") +
scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 11 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 11 rows containing missing values (`geom_point()`).
ancova(formula = Factor_H_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_H_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_H_ng_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 1303156.12 3 434385.37 12.5751121 < .0000001
Age 704472.05 1 704472.05 20.3939073 0.0000077
sex 1442818.12 1 1442818.12 41.7684407 < .0000001
bmi 387713.31 1 387713.31 11.2239929 0.0008630
E4_Positive 16993.22 1 16993.22 0.4919403 0.4833603
Residuals 1.896425e+7 549 34543.26
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -137.07712 25.11910 549.0000 -5.4570874 0.0000004
- A+T- 22.39103 24.59393 549.0000 0.9104292 1.0000000
- A+T+ -35.08715 23.09596 549.0000 -1.5191898 0.7757423
A-T+ - A+T- 159.46815 28.96439 549.0000 5.5056614 0.0000003
- A+T+ 101.98996 26.35928 549.0000 3.8692242 0.0007338
A+T- - A+T+ -57.47818 25.77966 549.0000 -2.2295942 0.1570685
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))3.10. MIF_AT
(MIF_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = MIF_pg_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "MIF (pg/mL)") +
scale_y_continuous(breaks = seq(0,40000, by = 10000), limits = c(0, 40000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = MIF_pg_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = MIF_pg_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - MIF_pg_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
A_N_cat 2.308263e+9 3 7.694208e+8 83.94832651 < .0000001
Age 9.168965e+7 1 9.168965e+7 10.00387882 0.0016475
sex 6438723.7 1 6438723.7 0.70250252 0.4023049
bmi 595638.3 1 595638.3 0.06498763 0.7988741
E4_Positive 5420026.5 1 5420026.5 0.59135668 0.4422221
Residuals 5.086803e+9 555 9165410.1
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -5138.5102 404.3632 555.0000 -12.707661 < .0000001
- A+T- 784.8591 400.0465 555.0000 1.961920 0.3016271
- A+T+ -3459.0114 372.8119 555.0000 -9.278167 < .0000001
A-T+ - A+T- 5923.3693 467.6787 555.0000 12.665468 < .0000001
- A+T+ 1679.4988 424.3034 555.0000 3.958250 0.0005119
A+T- - A+T+ -4243.8704 418.3905 555.0000 -10.143323 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))3.11. TNFR1_AT
(TNFR1_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = TNFR1_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,2.0, by = 0.5), limits = c(0, 2.0)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = TNFR1_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR1_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR1_ng_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 3.646631901 3 1.215543967 73.2990299 < .0000001
Age 0.721807768 1 0.721807768 43.5260349 < .0000001
sex 0.019676374 1 0.019676374 1.1865133 0.2765093
bmi 0.007548949 1 0.007548949 0.4552124 0.5001525
E4_Positive 0.021691689 1 0.021691689 1.3080397 0.2532456
Residuals 9.154012961 552 0.016583357
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -0.21115012 0.01729863 552.0000 -12.206175 < .0000001
- A+T- 0.06312776 0.01702940 552.0000 3.706987 0.0013854
- A+T+ -0.07183501 0.01592110 552.0000 -4.511939 0.0000471
A-T+ - A+T- 0.27427788 0.01995608 552.0000 13.744073 < .0000001
- A+T+ 0.13931511 0.01813478 552.0000 7.682206 < .0000001
A+T- - A+T+ -0.13496277 0.01783722 552.0000 -7.566356 < .0000001
──────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))3.12. TNFR2_AT
(TNFR2_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = TNFR2_ng_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR2 (ng/mL)") +
scale_y_continuous(breaks = seq(0,4, by = 1), limits = c(0, 4)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = TNFR2_ng_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR2_ng_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR2_ng_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
A_N_cat 13.06104150 3 4.35368050 54.4331308 < .0000001
Age 4.31650811 1 4.31650811 53.9683725 < .0000001
sex 0.83879660 1 0.83879660 10.4872935 0.0012741
bmi 0.11696311 1 0.11696311 1.4623647 0.2270710
E4_Positive 0.01144535 1 0.01144535 0.1430988 0.7053648
Residuals 44.23014586 553 0.07998218
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -0.3879975 0.03786755 553.0000 -10.246176 < .0000001
- A+T- 0.1075146 0.03761447 553.0000 2.858331 0.0265156
- A+T+ -0.1949528 0.03494497 553.0000 -5.578850 0.0000002
A-T+ - A+T- 0.4955121 0.04381503 553.0000 11.309182 < .0000001
- A+T+ 0.1930448 0.03965328 553.0000 4.868318 0.0000088
A+T- - A+T+ -0.3024674 0.03917350 553.0000 -7.721223 < .0000001
──────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))3.13. ICAM1_AT
(ICAM1_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = ICAM1_ng_mL,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "ICAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,8, by = 2), limits = c(0, 8)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 8 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 8 rows containing missing values (`geom_point()`).
ancova(formula = ICAM1_ng_mL ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = ICAM1_ng_mL ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - ICAM1_ng_mL
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
A_N_cat 51.355256 3 17.1184185 36.634494 < .0000001
Age 12.945826 1 12.9458258 27.704883 0.0000002
sex 6.566058 1 6.5660578 14.051777 0.0001967
bmi 4.264535 1 4.2645347 9.126373 0.0026366
E4_Positive 1.895130 1 1.8951297 4.055697 0.0445090
Residuals 257.001782 550 0.4672760
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
─────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -0.8444095 0.09282040 550.0000 -9.097240 < .0000001
- A+T- 0.1030773 0.09034505 550.0000 1.140929 1.0000000
- A+T+ -0.4141334 0.08433119 550.0000 -4.910797 0.0000072
A-T+ - A+T- 0.9474868 0.10683788 550.0000 8.868454 < .0000001
- A+T+ 0.4302761 0.09737519 550.0000 4.418745 0.0000718
A+T- - A+T+ -0.5172107 0.09461961 550.0000 -5.466210 0.0000004
─────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))3.14. VCAM1_AT
(VCAM1_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = VCAM1_ng_mL,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "VCAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,20, by = 5), limits = c(0, 20)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 10 rows containing missing values (`geom_point()`).
ancova(formula = VCAM1_ng_mL ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = VCAM1_ng_mL ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - VCAM1_ng_mL
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
A_N_cat 317.411966 3 105.803989 39.482390 < .0000001
Age 109.152263 1 109.152263 40.731850 < .0000001
sex 49.125503 1 49.125503 18.331939 0.0000219
bmi 4.733710 1 4.733710 1.766457 0.1843722
E4_Positive 10.449315 1 10.449315 3.899323 0.0488053
Residuals 1473.877184 550 2.679777
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -2.0328349 0.2208468 550.0000 -9.204729 < .0000001
- A+T- 0.5533835 0.2164406 550.0000 2.556745 0.0649981
- A+T+ -0.4394425 0.2025179 550.0000 -2.169895 0.1826532
A-T+ - A+T- 2.5862185 0.2551593 550.0000 10.135702 < .0000001
- A+T+ 1.5933924 0.2329813 550.0000 6.839143 < .0000001
A+T- - A+T+ -0.9928261 0.2269212 550.0000 -4.375203 0.0000871
────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))3.15. CRP_AT
(CRP_A_T_plot <- ggplot(FC_thesis_m,
aes(x = A_N_cat, y = CRP_pg_ml,
colour = A_N_cat, fill = A_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "CRP (pg/mL)") +
scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
scale_colour_manual(values = c("black","black", "black", "black")) +
scale_fill_brewer(palette = "Accent") +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 18 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 18 rows containing missing values (`geom_point()`).
ancova(formula = CRP_pg_ml ~ A_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = CRP_pg_ml ~ A_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - CRP_pg_ml
─────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────
A_N_cat 2.590924e +8 3 8.636413e+7 1.398154 0.2424422
Age 2.156456e +8 1 2.156456e+8 3.491101 0.0622339
sex 6.950520e +7 1 6.950520e+7 1.125224 0.2892645
bmi 1.496798e +9 1 1.496798e+9 24.231760 0.0000011
E4_Positive 9.126016e +8 1 9.126016e+8 14.774165 0.0001354
Residuals 3.378824e+10 547 6.177009e+7
─────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - A_N_cat
─────────────────────────────────────────────────────────────────────────────────────────────────────
A_N_cat A_N_cat Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────────────
A-T- - A-T+ -2117.5409 1054.0482 547.0000 -2.0089601 0.2701933
- A+T- -778.3078 1046.6535 547.0000 -0.7436156 1.0000000
- A+T+ -504.3118 972.0756 547.0000 -0.5187989 1.0000000
A-T+ - A+T- 1339.2330 1217.0876 547.0000 1.1003588 1.0000000
- A+T+ 1613.2291 1101.3200 547.0000 1.4648142 0.8612768
A+T- - A+T+ 273.9961 1090.1601 547.0000 0.2513356 1.0000000
─────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(A_N_cat)%>%
summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))4. Diagnosis scheme
4.1. YKL40_Diag
(YKL40_diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = YKL40_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "YKL40 (ng/mL)") +
scale_y_continuous(breaks = seq(0,1000, by = 250), limits = c(0,1000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = YKL40_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = YKL40_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - YKL40_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
Diag 2178.25068 1 2178.25068 0.27879152 0.5977062
Age 939854.84656 1 939854.84656 120.29081958 < .0000001
sex 80.68115 1 80.68115 0.01032628 0.9190965
bmi 10444.88970 1 10444.88970 1.33682807 0.2480934
E4_Positive 34990.98453 1 34990.98453 4.47845135 0.0347710
Residuals 4320693.23306 553 7813.18849
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
─────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI 7.207481 13.65035 553.0000 0.5280071 0.5977062
─────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))4.2. Axl_Diag
(AXL_diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = AXL_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "AXL (ng/mL)") +
scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 6 rows containing missing values (`geom_point()`).
ancova(formula = AXL_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = AXL_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - AXL_ng_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
Diag 0.02801677 1 0.02801677 9.370277e-4 0.9755908
Age 104.36977383 1 104.36977383 3.490671886 0.0622422
sex 0.18456852 1 0.18456852 0.006172938 0.9374045
bmi 110.56299606 1 110.56299606 3.697805675 0.0549954
E4_Positive 4.98603189 1 4.98603189 0.166759021 0.6831659
Residuals 16594.29083373 555 29.89962312
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
────────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.02586450 0.8449438 555.0000 -0.03061091 0.9755908
────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))4.3. Tyro3_Diag
(Tyro3_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = Tyro3_pg_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Tyro3 (pg/mL)") +
scale_y_continuous(breaks = seq(0,8000, by = 2000), limits = c(0,8000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = Tyro3_pg_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Tyro3_pg_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Tyro3_pg_ml
────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────
Diag 4889414 1 4889414 4.833141 0.0283305
Age 5460429 1 5460429 5.397583 0.0205258
sex 1709778 1 1709778 1.690100 0.1941284
bmi 4167860 1 4167860 4.119891 0.0428592
E4_Positive 2221818 1 2221818 2.196247 0.1389158
Residuals 5.604504e+8 554 1011643
────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
─────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -341.4658 155.3218 554.0000 -2.198441 0.0283305
─────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))4.4. TREM2_Diag
(TREM2_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = TREM2_pg_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TREM2 (pg/mL)") +
scale_y_continuous(breaks = seq(0,15000, by = 3000), limits = c(0,15000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = TREM2_pg_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TREM2_pg_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TREM2_pg_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
Diag 140373.3 1 140373.3 0.04698870 0.8284682
Age 1.036283e+8 1 1.036283e+8 34.68864302 < .0000001
sex 238435.1 1 238435.1 0.07981400 0.7776550
bmi 1.911271e+7 1 1.911271e+7 6.39780654 0.0117019
E4_Positive 2146138.2 1 2146138.2 0.71840040 0.3970345
Residuals 1.657998e+9 555 2987384.5
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -57.85694 266.9062 555.0000 -0.2167688 0.8284682
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))4.5. C1q_Diag
(C1q_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = C1q_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C1q (ng/mL)") +
scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C1q_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C1q_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C1q_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
Diag 2.160122 1 2.160122 5.900046e-4 0.9806301
Age 204547.414699 1 204547.414699 55.86903408 < .0000001
sex 66064.575723 1 66064.575723 18.04454013 0.0000253
bmi 988.735651 1 988.735651 0.27005820 0.6035011
E4_Positive 191.448122 1 191.448122 0.05229116 0.8192081
Residuals 2020979.506500 552 3661.194758
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
───────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.2294611 9.446726 552.0000 -0.02429001 0.9806301
───────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))4.6. C3_Diag
(C3_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = C3_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C3 (ng/mL)") +
scale_y_continuous(breaks = seq(0,20000, by = 5000), limits = c(0, 20000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C3_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C3_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C3_ng_ml
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
Diag 9643561 1 9643561 0.8711779 0.3510390
Age 1.824320e+8 1 1.824320e+8 16.4805048 0.0000563
sex 1.431722e+8 1 1.431722e+8 12.9338604 0.0003517
bmi 1.053531e+7 1 1.053531e+7 0.9517361 0.3297067
E4_Positive 1.250038e+7 1 1.250038e+7 1.1292561 0.2884004
Residuals 6.088261e+9 550 1.106957e+7
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -484.2888 518.8610 550.0000 -0.9333691 0.3510390
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))4.7. C4_Diag
(C4_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = C4_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C4 (ng/mL)") +
scale_y_continuous(breaks = seq(0,4000, by = 1000), limits = c(0, 4000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 15 rows containing missing values (`geom_point()`).
ancova(formula = C4_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C4_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C4_ng_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
Diag 8600.522 1 8600.522 0.03699994 0.8475367
Age 4107042.650 1 4107042.650 17.66873140 0.0000307
sex 6825419.216 1 6825419.216 29.36334222 < .0000001
bmi 1543558.285 1 1543558.285 6.64047566 0.0102301
E4_Positive 22674.709 1 22674.709 0.09754789 0.7549116
Residuals 1.269160e+8 546 232446.946
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -14.46478 75.19887 546.0000 -0.1923537 0.8475367
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))4.8. FB_Diag
(FB_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = Factor_B_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor B (ng/mL)") +
scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 14 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 14 rows containing missing values (`geom_point()`).
ancova(formula = Factor_B_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_B_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_B_ng_ml
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
Diag 23907.63 1 23907.63 0.4811229 0.4882080
Age 308384.32 1 308384.32 6.2060012 0.0130266
sex 1023771.56 1 1023771.56 20.6026279 0.0000070
bmi 540369.11 1 540369.11 10.8745195 0.0010382
E4_Positive 95422.92 1 95422.92 1.9203141 0.1663857
Residuals 2.723084e+7 548 49691.31
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
─────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI 24.31950 35.06119 548.0000 0.6936302 0.4882080
─────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))NA4.9. FH_Diag
(FH_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = Factor_H_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor H (ng/mL)") +
scale_y_continuous(breaks = seq(0,1500, by = 500), limits = c(0, 1500)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 11 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 11 rows containing missing values (`geom_point()`).
ancova(formula = Factor_H_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_H_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_H_ng_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
Diag 5143.003 1 5143.003 0.13985578 0.7085683
Age 1150111.334 1 1150111.334 31.27544756 < .0000001
sex 1254748.212 1 1254748.212 34.12088095 < .0000001
bmi 228913.715 1 228913.715 6.22494420 0.0128879
E4_Positive 2407.201 1 2407.201 0.06546001 0.7981610
Residuals 2.026226e+7 551 36773.617
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -11.18613 29.91159 551.0000 -0.3739730 0.7085683
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))4.10. MIF_Diag
(MIF_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = MIF_pg_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "MIF (pg/mL)") +
scale_y_continuous(breaks = seq(0,30000, by = 5000), limits = c(0, 30000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = MIF_pg_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = MIF_pg_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - MIF_pg_ml
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
Diag 9884501 1 9884501 0.7455020 0.3882757
Age 5.050644e+8 1 5.050644e+8 38.0926159 < .0000001
sex 4778507 1 4778507 0.3604013 0.5485276
bmi 4.916344e+7 1 4.916344e+7 3.7079711 0.0546619
E4_Positive 5.439225e+7 1 5.439225e+7 4.1023348 0.0433004
Residuals 7.385181e+9 557 1.325885e+7
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -485.4564 562.2452 557.0000 -0.8634246 0.3882757
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))4.11. TNFR1_Diag
(TNFR1_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = TNFR1_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,1.5, by = 0.5), limits = c(0, 1.5)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = TNFR1_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR1_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR1_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
Diag 0.06730994 1 0.06730994 2.928510519 0.0875875
Age 1.38125890 1 1.38125890 60.095602298 < .0000001
sex 2.367392e-4 1 2.367392e-4 0.010300015 0.9191990
bmi 0.02320891 1 0.02320891 1.009769589 0.3153967
E4_Positive 4.700320e-5 1 4.700320e-5 0.002045008 0.9639468
Residuals 12.73333492 554 0.02298436
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
───────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.04006841 0.02341417 554.0000 -1.711289 0.0875875
───────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))4.12. TNFR2_Diag
(TNFR2_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = TNFR2_ng_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR2 (ng/mL)") +
scale_y_continuous(breaks = seq(0,3, by = 1), limits = c(0, 3)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = TNFR2_ng_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR2_ng_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR2_ng_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
Diag 0.008924975 1 0.008924975 0.08647286 0.7688199
Age 8.882838221 1 8.882838221 86.06460372 < .0000001
sex 0.347462617 1 0.347462617 3.36651773 0.0670693
bmi 0.010633942 1 0.010633942 0.10303081 0.7483432
E4_Positive 0.163969531 1 0.163969531 1.58867834 0.2080447
Residuals 57.282262388 555 0.103211284
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
────────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.01459129 0.04961967 555.0000 -0.2940627 0.7688199
────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))4.13. ICAM1_Diag
(ICAM1_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = ICAM1_ng_mL,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "ICAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,6, by = 2), limits = c(0, 6)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 8 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 8 rows containing missing values (`geom_point()`).
ancova(formula = ICAM1_ng_mL ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = ICAM1_ng_mL ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - ICAM1_ng_mL
───────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────
Diag 0.31004951 1 0.31004951 0.5555884 0.4563605
Age 28.56166520 1 28.56166520 51.1806310 < .0000001
sex 4.05172132 1 4.05172132 7.2604189 0.0072638
bmi 1.35951899 1 1.35951899 2.4361689 0.1191386
E4_Positive 0.05670832 1 0.05670832 0.1016176 0.7500176
Residuals 308.04698755 552 0.55805614
───────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
───────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.08599105 0.1153657 552.0000 -0.7453780 0.4563605
───────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))4.14. VCAM1_Diag
(VCAM1_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = VCAM1_ng_mL,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "VCAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,15, by = 5), limits = c(0, 15)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 10 rows containing missing values (`geom_point()`).
ancova(formula = VCAM1_ng_mL ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = VCAM1_ng_mL ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - VCAM1_ng_mL
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
Diag 3.99222982 1 3.99222982 1.232985319 0.2673107
Age 167.18623494 1 167.18623494 51.634846254 < .0000001
sex 35.26317295 1 35.26317295 10.890899687 0.0010287
bmi 0.02548511 1 0.02548511 0.007870983 0.9293378
E4_Positive 7.15179805 1 7.15179805 2.208806203 0.1377957
Residuals 1787.29692025 552 3.23785674
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -0.3085378 0.2778622 552.0000 -1.110399 0.2673107
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))NA4.15. CRP_Diag
(CRP_Diag_plot <- ggplot(FC_thesis_m,
aes(x = Diag, y = CRP_pg_ml,
colour = Diag, fill = Diag)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "CRP (pg/mL)") +
scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
scale_colour_manual(values = c("black", "black" )) +
scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 18 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 18 rows containing missing values (`geom_point()`).
ancova(formula = CRP_pg_ml ~ Diag +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = CRP_pg_ml ~ Diag,
postHocCorr = "bonf")
ANCOVA
ANCOVA - CRP_pg_ml
──────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────
Diag 2.098755e +7 1 2.098755e+7 0.3386248 0.5608629
Age 3.359206e +8 1 3.359206e+8 5.4199297 0.0202705
sex 6.388868e +7 1 6.388868e+7 1.0308155 0.3104144
bmi 1.425240e +9 1 1.425240e+9 22.9956190 0.0000021
E4_Positive 9.444540e +8 1 9.444540e+8 15.2383467 0.0001065
Residuals 3.402635e+10 549 6.197877e+7
──────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - Diag
──────────────────────────────────────────────────────────────────────────────────────────────
Diag Diag Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────
SCD - MCI -714.2891 1227.481 549.0000 -0.5819148 0.5608629
──────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(Diag)%>%
summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))5. Diagnosis+T scheme
5.1. YKL40_Diag+T
(YKL40_diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = YKL40_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "YKL40 (ng/mL)") +
scale_y_continuous(breaks = seq(0,1000, by = 200), limits = c(0,800)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = YKL40_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = YKL40_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - YKL40_ng_ml
─────────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 676408.77563 3 225469.59188 34.069660125 < .0000001
Age 462957.73400 1 462957.73400 69.955387413 < .0000001
sex 5138.72489 1 5138.72489 0.776488789 0.3786014
bmi 14.36535 1 14.36535 0.002170681 0.9628564
E4_Positive 2270.64126 1 2270.64126 0.343106027 0.5582817
Residuals 3646462.70811 551 6617.89965
─────────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -46.60315 35.838428 551.0000 -1.3003682 1.0000000
- MCI_T- 23.67505 13.540945 551.0000 1.7484047 0.4857053
- MCI_T+ -56.62232 14.641930 551.0000 -3.8671354 0.0007396
SCD_T+ - MCI_T- 70.27820 33.806826 551.0000 2.0788169 0.2285775
- MCI_T+ -10.01917 33.927170 551.0000 -0.2953141 1.0000000
MCI_T- - MCI_T+ -80.29738 7.987080 551.0000 -10.0534089 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))NA5.2. Axl_Diag+T
(AXL_diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = AXL_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "AXL (ng/mL)") +
scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 6 rows containing missing values (`geom_point()`).
ancova(formula = AXL_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = AXL_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - AXL_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 1977.57437 3 659.19146 24.9394025 < .0000001
Age 16.83151 1 16.83151 0.6367920 0.4252174
sex 29.29865 1 29.29865 1.1084652 0.2928756
bmi 26.83254 1 26.83254 1.0151641 0.3141095
E4_Positive 106.65611 1 106.65611 4.0351549 0.0450472
Residuals 14616.74449 553 26.43173
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -3.7509932 2.2647858 553.0000 -1.65622429 0.5894620
- MCI_T- 0.6808976 0.8558796 553.0000 0.79555304 1.0000000
- MCI_T+ -3.6477524 0.9272126 553.0000 -3.93410561 0.0005648
SCD_T+ - MCI_T- 4.4318908 2.1364310 553.0000 2.07443672 0.2310048
- MCI_T+ 0.1032408 2.1447097 553.0000 0.04813742 1.0000000
MCI_T- - MCI_T+ -4.3286500 0.5063278 553.0000 -8.54910672 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))5.3. Tyro3_Diag+T
(Tyro3_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = Tyro3_pg_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Tyro3 (pg/mL)") +
scale_y_continuous(breaks = seq(0,12000, by = 2000), limits = c(0,10000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = Tyro3_pg_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Tyro3_pg_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Tyro3_pg_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 9.133402e+7 3 3.044467e+7 35.4541245 < .0000001
Age 334262.8 1 334262.8 0.3892633 0.5329446
sex 100838.3 1 100838.3 0.1174305 0.7319687
bmi 720390.5 1 720390.5 0.8389256 0.3601042
E4_Positive 1176916.3 1 1176916.3 1.3705695 0.2422192
Residuals 4.740057e+8 552 858706.1
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -621.2965 408.23172 552.0000 -1.521921 0.7716087
- MCI_T- -166.2112 154.26957 552.0000 -1.077407 1.0000000
- MCI_T+ -1072.7427 166.71547 552.0000 -6.434572 < .0000001
SCD_T+ - MCI_T- 455.0854 385.08938 552.0000 1.181766 1.0000000
- MCI_T+ -451.4461 386.38380 552.0000 -1.168388 1.0000000
MCI_T- - MCI_T+ -906.5315 90.92031 552.0000 -9.970616 < .0000001
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))5.4. TREM2_Diag+T
(TREM2_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = TREM2_pg_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TREM2 (pg/mL)") +
scale_y_continuous(breaks = seq(0,14000, by = 2000), limits = c(0,14000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 10 rows containing missing values (`geom_point()`).
ancova(formula = TREM2_pg_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TREM2_pg_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TREM2_pg_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 1.218727e+8 3 4.062422e+7 14.6232455 < .0000001
Age 4.818147e+7 1 4.818147e+7 17.3435805 0.0000362
sex 2993149 1 2993149 1.0774249 0.2997280
bmi 1.115280e+7 1 1.115280e+7 4.0146035 0.0455952
E4_Positive 2468594 1 2468594 0.8886042 0.3462675
Residuals 1.536266e+9 553 2778058
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ 1223.8919 734.2606 553.0000 1.666836 0.5766796
- MCI_T- 390.2147 277.4301 553.0000 1.406533 0.9607649
- MCI_T+ -647.4559 300.0788 553.0000 -2.157620 0.1883241
SCD_T+ - MCI_T- -833.6772 692.6232 553.0000 -1.203652 1.0000000
- MCI_T+ -1871.3479 695.0550 553.0000 -2.692374 0.0438574
MCI_T- - MCI_T+ -1037.6707 163.8291 553.0000 -6.333859 < .0000001
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))5.5. C1q_Diag+T
(C1q_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = C1q_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C1q (ng/mL)") +
scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C1q_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C1q_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C1q_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 191116.144 3 63705.381 19.147833 < .0000001
Age 91220.214 1 91220.214 27.417926 0.0000002
sex 90567.525 1 90567.525 27.221748 0.0000003
bmi 7304.208 1 7304.208 2.195415 0.1389946
E4_Positive 12907.342 1 12907.342 3.879541 0.0493793
Residuals 1829865.522 550 3327.028
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -26.660567 27.528961 550.0000 -0.9684553 1.0000000
- MCI_T- 8.702497 9.602253 550.0000 0.9062974 1.0000000
- MCI_T+ -34.279227 10.398528 550.0000 -3.2965461 0.0062517
SCD_T+ - MCI_T- 35.363063 26.231667 550.0000 1.3481058 1.0000000
- MCI_T+ -7.618660 26.295254 550.0000 -0.2897352 1.0000000
MCI_T- - MCI_T+ -42.981723 5.693093 550.0000 -7.5498016 < .0000001
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))5.6. C3_Diag+T
(C3_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = C3_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C3 (ng/mL)") +
scale_y_continuous(breaks = seq(0,20000, by = 2000), limits = c(0, 20000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 9 rows containing missing values (`geom_point()`).
ancova(formula = C3_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C3_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C3_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 2.189505e+7 3 7298351 0.6582439 0.5780339
Age 1.498339e+8 1 1.498339e+8 13.5136382 0.0002601
sex 1.507848e+8 1 1.507848e+8 13.5993965 0.0002488
bmi 1.144152e+7 1 1.144152e+7 1.0319191 0.3101566
E4_Positive 6785434 1 6785434 0.6119835 0.4343799
Residuals 6.076010e+9 548 1.108761e+7
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ 813.2021 1469.2142 548.0000 0.5534946 1.0000000
- MCI_T- -295.9628 560.6845 548.0000 -0.5278598 1.0000000
- MCI_T+ -580.9119 604.3388 548.0000 -0.9612355 1.0000000
SCD_T+ - MCI_T- -1109.1649 1383.9049 548.0000 -0.8014749 1.0000000
- MCI_T+ -1394.1140 1388.7738 548.0000 -1.0038452 1.0000000
MCI_T- - MCI_T+ -284.9491 326.9804 548.0000 -0.8714561 1.0000000
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))NA5.7. C4_Diag+T
(C4_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = C4_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "C4 (ng/mL)") +
scale_y_continuous(breaks = seq(0,5000, by = 500), limits = c(0, 4000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 15 rows containing missing values (`geom_point()`).
ancova(formula = C4_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = C4_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - C4_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 3623282.7 3 1207760.9 5.3285866 0.0012641
Age 2025844.2 1 2025844.2 8.9379333 0.0029194
sex 7871388.0 1 7871388.0 34.7282089 < .0000001
bmi 2053913.6 1 2053913.6 9.0617742 0.0027312
E4_Positive 138855.7 1 138855.7 0.6126251 0.4341417
Residuals 1.233014e+8 544 226656.9
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ 100.48312 210.13939 544.0000 0.4781737 1.0000000
- MCI_T- 51.06770 80.16622 544.0000 0.6370227 1.0000000
- MCI_T+ -134.64055 86.61141 544.0000 -1.5545358 0.7238293
SCD_T+ - MCI_T- -49.41542 197.92972 544.0000 -0.2496615 1.0000000
- MCI_T+ -235.12367 198.61980 544.0000 -1.1837877 1.0000000
MCI_T- - MCI_T+ -185.70824 47.10848 544.0000 -3.9421401 0.0005478
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))5.8. FB_Diag+T
(FB_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = Factor_B_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor B (ng/mL)") +
scale_y_continuous(breaks = seq(0,2500, by = 500), limits = c(0, 2000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 14 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 14 rows containing missing values (`geom_point()`).
ancova(formula = Factor_B_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_B_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_B_ng_ml
────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 337916.6 3 112638.88 2.284847 0.0779273
Age 147941.0 1 147941.03 3.000941 0.0837809
sex 1150590.0 1 1150590.01 23.339382 0.0000018
bmi 612887.4 1 612887.36 12.432241 0.0004575
E4_Positive 197294.9 1 197294.86 4.002069 0.0459391
Residuals 2.691683e+7 546 49298.22
────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ 63.053629 98.11009 546.0000 0.6426824 1.0000000
- MCI_T- 47.987371 37.75634 546.0000 1.2709750 1.0000000
- MCI_T+ -4.849798 40.65129 546.0000 -0.1193024 1.0000000
SCD_T+ - MCI_T- -15.066258 92.30935 546.0000 -0.1632149 1.0000000
- MCI_T+ -67.903427 92.58726 546.0000 -0.7333992 1.0000000
MCI_T- - MCI_T+ -52.837169 21.90318 546.0000 -2.4123057 0.0970840
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))5.9. FH_Diag+T
(FH_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = Factor_H_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "Factor H (ng/mL)") +
scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 11 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 11 rows containing missing values (`geom_point()`).
ancova(formula = Factor_H_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = Factor_H_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - Factor_H_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 779021.66 3 259673.89 7.315176 0.0000814
Age 569803.37 1 569803.37 16.051718 0.0000701
sex 1470142.07 1 1470142.07 41.414823 < .0000001
bmi 348077.47 1 348077.47 9.805560 0.0018325
E4_Positive 88441.85 1 88441.85 2.491462 0.1150413
Residuals 1.948838e+7 549 35497.97
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -44.191117 83.15478 549.0000 -0.5314321 1.0000000
- MCI_T- 7.073125 31.71740 549.0000 0.2230046 1.0000000
- MCI_T+ -79.736823 34.31485 549.0000 -2.3236824 0.1230405
SCD_T+ - MCI_T- 51.264242 78.30999 549.0000 0.6546322 1.0000000
- MCI_T+ -35.545707 78.58893 549.0000 -0.4522992 1.0000000
MCI_T- - MCI_T+ -86.809949 18.64182 549.0000 -4.6567325 0.0000242
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))5.10. MIF_Diag+T
(MIF_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = MIF_pg_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "MIF (pg/mL)") +
scale_y_continuous(breaks = seq(0,30000, by = 5000), limits = c(0, 30000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = MIF_pg_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = MIF_pg_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - MIF_pg_ml
───────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
───────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 2.142031e+9 3 7.140102e+8 75.43747582 < .0000001
Age 5.498328e+7 1 5.498328e+7 5.80916068 0.0162671
sex 5943659.4 1 5943659.4 0.62796674 0.4284407
bmi 669239.7 1 669239.7 0.07070733 0.7904084
E4_Positive 3.191094e+7 1 3.191094e+7 3.37149398 0.0668683
Residuals 5.253034e+9 555 9464927.0
───────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -5261.69622 1355.2713 555.0000 -3.8823932 0.0006953
- MCI_T- 80.72698 512.0856 555.0000 0.1576435 1.0000000
- MCI_T+ -4334.10080 553.2709 555.0000 -7.8335957 < .0000001
SCD_T+ - MCI_T- 5342.42319 1278.5130 555.0000 4.1786226 0.0002044
- MCI_T+ 927.59542 1282.8572 555.0000 0.7230699 1.0000000
MCI_T- - MCI_T+ -4414.82777 301.5173 555.0000 -14.6420380 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))5.11. TNFR1_Diag+T
(TNFR1_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = TNFR1_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,1.5, by = 0.5), limits = c(0, 1.5)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 7 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 7 rows containing missing values (`geom_point()`).
ancova(formula = TNFR1_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR1_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR1_ng_ml
──────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
──────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 2.503136092 3 0.834378697 44.7270356 < .0000001
Age 0.406318190 1 0.406318190 21.7807672 0.0000038
sex 0.022223111 1 0.022223111 1.1912742 0.2755488
bmi 0.003024713 1 0.003024713 0.1621403 0.6873494
E4_Positive 0.189101173 1 0.189101173 10.1368059 0.0015353
Residuals 10.297508770 552 0.018654907
──────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -0.168037445 0.06016673 552.0000 -2.79286332 0.0324337
- MCI_T- -0.019804296 0.02273510 552.0000 -0.87108887 1.0000000
- MCI_T+ -0.170227637 0.02460406 552.0000 -6.91868041 < .0000001
SCD_T+ - MCI_T- 0.148233149 0.05675697 552.0000 2.61171726 0.0555241
- MCI_T+ -0.002190192 0.05695904 552.0000 -0.03845205 1.0000000
MCI_T- - MCI_T+ -0.150423341 0.01344958 552.0000 -11.18423762 < .0000001
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))NA5.12. TNFR2_Diag+T
(TNFR2_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = TNFR2_ng_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "TNFR2 (ng/mL)") +
scale_y_continuous(breaks = seq(0,3, by = 1), limits = c(0, 3)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 5 rows containing missing values (`geom_point()`).
ancova(formula = TNFR2_ng_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = TNFR2_ng_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - TNFR2_ng_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 10.7022603 3 3.56742011 42.344468 < .0000001
Age 3.3647952 1 3.36479517 39.939356 < .0000001
sex 0.8183835 1 0.81838348 9.714026 0.0019236
bmi 0.1047269 1 0.10472693 1.243085 0.2653616
E4_Positive 0.2725944 1 0.27259445 3.235634 0.0725975
Residuals 46.5889270 553 0.08424761
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -0.38144328 0.12786441 553.0000 -2.9831857 0.0178711
- MCI_T- 0.02416267 0.04832385 553.0000 0.5000154 1.0000000
- MCI_T+ -0.28881112 0.05224244 553.0000 -5.5282853 0.0000003
SCD_T+ - MCI_T- 0.40560595 0.12062432 553.0000 3.3625553 0.0049540
- MCI_T+ 0.09263217 0.12103809 553.0000 0.7653142 1.0000000
MCI_T- - MCI_T+ -0.31297378 0.02851557 553.0000 -10.9755414 < .0000001
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))5.13. ICAM1_Diag+T
(ICAM1_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = ICAM1_ng_mL,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "ICAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,7.5, by = 2.5), limits = c(0, 7.5)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 8 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 8 rows containing missing values (`geom_point()`).
ancova(formula = ICAM1_ng_mL ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = ICAM1_ng_mL ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - ICAM1_ng_mL
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 42.013524 3 14.0045079 28.919343 < .0000001
Age 10.248680 1 10.2486796 21.163548 0.0000052
sex 7.107285 1 7.1072850 14.676561 0.0001423
bmi 3.918148 1 3.9181481 8.090985 0.0046142
E4_Positive 4.245099 1 4.2450985 8.766139 0.0032012
Residuals 266.343513 550 0.4842609
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -0.45348496 0.30656733 550.0000 -1.4792345 0.8379010
- MCI_T- 0.03165420 0.11583583 550.0000 0.2732678 1.0000000
- MCI_T+ -0.60065331 0.12538051 550.0000 -4.7906433 0.0000128
SCD_T+ - MCI_T- 0.48513916 0.28918036 550.0000 1.6776353 0.5639187
- MCI_T+ -0.14716835 0.29026137 550.0000 -0.5070201 1.0000000
MCI_T- - MCI_T+ -0.63230751 0.06863242 550.0000 -9.2129569 < .0000001
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))5.14. VCAM1_Diag+T
(VCAM1_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = VCAM1_ng_mL,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "VCAM1 (ng/mL)") +
scale_y_continuous(breaks = seq(0,20, by = 5), limits = c(0, 20)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 10 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 10 rows containing missing values (`geom_point()`).
ancova(formula = VCAM1_ng_mL ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = VCAM1_ng_mL ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - VCAM1_ng_mL
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 180.328676 3 60.109559 20.522078 < .0000001
Age 70.960108 1 70.960108 24.226578 0.0000011
sex 52.951232 1 52.951232 18.078145 0.0000249
bmi 3.377784 1 3.377784 1.153213 0.2833484
E4_Positive 39.346281 1 39.346281 13.433262 0.0002712
Residuals 1610.960474 550 2.929019
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -1.06751671 0.7539808 550.0000 -1.4158407 0.9443291
- MCI_T- -0.08415319 0.2848856 550.0000 -0.2953929 1.0000000
- MCI_T+ -1.37819131 0.3081515 550.0000 -4.4724470 0.0000564
SCD_T+ - MCI_T- 0.98336352 0.7112106 550.0000 1.3826615 1.0000000
- MCI_T+ -0.31067460 0.7138794 550.0000 -0.4351920 1.0000000
MCI_T- - MCI_T+ -1.29403812 0.1685257 550.0000 -7.6785826 < .0000001
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))5.15. CRP_Diag+T
(CRP_Diag_T_plot <- ggplot(FC_thesis_m,
aes(x = clinical_N_cat, y = CRP_pg_ml,
colour = clinical_N_cat, fill = clinical_N_cat)) +
geom_boxplot() +
geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
labs(x = NULL, y = "CRP (pg/mL)") +
scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
scale_colour_manual(values = c("black",'black', "black", "black")) +
scale_fill_manual(values = c("lightblue", "darkorange3",
"lightgreen", "coral")) +
theme_bw() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))Warning: Removed 18 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 18 rows containing missing values (`geom_point()`).
ancova(formula = CRP_pg_ml ~ clinical_N_cat +
Age +
sex +
bmi +
E4_Positive,
data = FC_thesis_m,
postHoc = CRP_pg_ml ~ clinical_N_cat,
postHocCorr = "bonf")
ANCOVA
ANCOVA - CRP_pg_ml
─────────────────────────────────────────────────────────────────────────────────────
Sum of Squares df Mean Square F p
─────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat 9.905780e +7 3 3.301927e+7 0.5320311 0.6604731
Age 2.156296e +8 1 2.156296e+8 3.4743853 0.0628614
sex 7.621540e +7 1 7.621540e+7 1.2280395 0.2682757
bmi 1.485933e +9 1 1.485933e+9 23.9424605 0.0000013
E4_Positive 1.017306e +9 1 1.017306e+9 16.3915919 0.0000590
Residuals 3.394828e+10 547 6.206266e+7
─────────────────────────────────────────────────────────────────────────────────────
POST HOC TESTS
Post Hoc Comparisons - clinical_N_cat
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
clinical_N_cat clinical_N_cat Mean Difference SE df t p-bonferroni
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
SCD_T- - SCD_T+ -1220.0488 3476.7279 547.0000 -0.35091867 1.0000000
- MCI_T- -635.8976 1326.3703 547.0000 -0.47942691 1.0000000
- MCI_T+ -1472.0645 1430.1943 547.0000 -1.02927586 1.0000000
SCD_T+ - MCI_T- 584.1512 3275.0280 547.0000 0.17836524 1.0000000
- MCI_T+ -252.0158 3285.2057 547.0000 -0.07671232 1.0000000
MCI_T- - MCI_T+ -836.1669 775.1875 547.0000 -1.07866404 1.0000000
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Note. Comparisons are based on estimated marginal meansFC_thesis_m%>%
group_by(clinical_N_cat)%>%
summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))---
title: "FACE_Analysis_Manual"
output: html_notebook
---

# 1. Load all libraries required

```{r}
library(readxl)
library(tidyverse)


library('dplyr')
library('readxl')
library(ggplot2)
library(dplyr) # required by custom function outlier_removal()
library(jmv) # ancova()
library(ggplot2) # ggplot()
library(gridExtra) # grid.arrange()
```

# 2. Pre analysis: Data import and cleaning
```{r}
manual_FACE<- read_excel('FACE_Thesis_Manual.xlsx')
manual_FACE <- data.frame(manual_FACE)

colnames(manual_FACE)
FC_thesis_manual <- subset(manual_FACE, select = c(CSF,
                                                   YKL40_ng_mL, sAxl_ng_mL, Tyro3_pg_mL,
                                                   sTREM2_pg_mL, C1q_ng_mL, C3_ng_mL, C4_ng_mL,
                                                   Factor.B_ng_mL, Factor.H_ng_mL, MIF_pg_mL,
                                                   TNFR1_ng_mL, TNFR2_ng_mL, sICAM1_ng_mL, sVCAM1_ng_mL,
                                                   CRP_pg_mL, Age_LP, Sex_1m_2f, BMI, APOE_Status, 
                                                   Diag_CSF, A_1pos_0neg, T_1pos_0neg, N_1pos_0neg, AT, AN))

FC_thesis_manual$A_N_cat <- factor(ifelse(FC_thesis_manual$AN == 0,
                                    "A-T-",
                                    ifelse(FC_thesis_manual$AN == 2,
                                           "A+T-",
                                           ifelse(FC_thesis_manual$AN == 1,
                                                  "A-T+",
                                                  "A+T+"))),
                             levels = c("A-T-", "A-T+", "A+T-", "A+T+"))
table(FC_thesis_manual$A_N_cat)

```


```{r}
FC_thesis_manual$Diag_CSF <- factor(FC_thesis_manual$Diag_CSF,
                                   levels = c("SCD", "MCI"))
table(FC_thesis_manual$A_N_cat)

table(FC_thesis_manual$Diag_CSF)
which(FC_thesis_manual$Diag_CSF == "SCD")
which(FC_thesis_manual$Diag_CSF == "SCD" & 
        FC_thesis_manual$N_1pos_0neg == 0)
which(FC_thesis_manual$Diag_CSF == "SCD" & 
        FC_thesis_manual$N_1pos_0neg == 1)

FC_thesis_manual$clinical_N_cat <- factor(ifelse(FC_thesis_manual$Diag_CSF == "SCD" & 
                                                                 FC_thesis_manual$N_1pos_0neg == 0,
                                                               "SCD_T-",
                                                               ifelse(FC_thesis_manual$Diag_CSF == "SCD" & 
                                                                        FC_thesis_manual$N_1pos_0neg == 1,
                                                                      "SCD_T+",
                                                                      ifelse(FC_thesis_manual$Diag_CSF == "MCI" & 
                                                                               FC_thesis_manual$N_1pos_0neg == 0,
                                                                             "MCI_T-",
                                                                             ifelse(FC_thesis_manual$Diag_CSF == "MCI" & 
                                                                                      FC_thesis_manual$N_1pos_0neg == 1,
                                                                                    "MCI_T+",'others')))),
                                          levels = c('SCD_T-', 'SCD_T+', "MCI_T-", "MCI_T+"))
table(FC_thesis_manual$clinical_N_cat)
```

Outlier removal function
Higher than or less than 3 standard deviations

```{r}
outlier_removal <- function(df) {
  find.outlier <- function(df) {
    if(!is.numeric(df)) {
      df
    }
    else {
      arith.mean <- mean(df, na.rm = TRUE)
      st.dev <- sd(df, na.rm = TRUE)
      df[which(df > (arith.mean + 3*st.dev) | df < (arith.mean - 3*st.dev))] <- NA
      df
    }
  }
  df %>% dplyr::mutate_all(find.outlier)
}
```

Removed outliers
```{r}
FC_thesis_manual_clean <- outlier_removal(FC_thesis_manual[1:16])
```

Check for merge outlier removed columns
```{r}
dim(FC_thesis_manual_clean)
FC_thesis_m <- merge(FC_thesis_manual_clean,
                     FC_thesis_manual[c(1, 17:ncol(FC_thesis_manual))],
                          by = "CSF")
dim(FC_thesis_m)
```

Did not Omit NAs
```{r}
table(FC_thesis_m$A_N_cat)
table(FC_thesis_m$Diag)
table(FC_thesis_m$clinical_N_cat)

colnames(FC_thesis_m)
FC_thesis_m$BMI <- as.numeric(as.character(FC_thesis_m$BMI)) 
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Age_LP'] <- 'Age'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Sex_1m_2f'] <- 'sex'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'BMI'] <- 'bmi'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sAxl_ng_mL'] <- 'AXL_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sTREM2_pg_mL'] <- 'TREM2_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Factor.B_ng_mL'] <- 'Factor_B_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Factor.H_ng_mL'] <- 'Factor_H_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sICAM1_ng_mL'] <- 'ICAM1_ng_mL'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'sVCAM1_ng_mL'] <- 'VCAM1_ng_mL'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'YKL40_ng_mL'] <- 'YKL40_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C1q_ng_mL'] <- 'C1q_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C3_ng_mL'] <- 'C3_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'C4_ng_mL'] <- 'C4_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'MIF_pg_mL'] <- 'MIF_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'TNFR1_ng_mL'] <- 'TNFR1_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'TNFR2_ng_mL'] <- 'TNFR2_ng_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'CRP_pg_mL'] <- 'CRP_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Tyro3_pg_mL'] <- 'Tyro3_pg_ml'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'APOE_Status'] <- 'E4_Positive'
colnames(FC_thesis_m)[colnames(FC_thesis_m) == 'Diag_CSF'] <- 'Diag'
colnames(FC_thesis_m)
```

Data is ready for analysis

# 3. AT scheme
## 3.1. YKL40_AT
```{r}
(YKL40_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = YKL40_ng_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "YKL40 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,1200, by = 200), limits = c(0,1200)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = YKL40_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = YKL40_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
            Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))

```

## 3.2. AXL_AT
```{r}
(AXL_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = AXL_ng_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "AXL (ng/mL)") +
    scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = AXL_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = AXL_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
            Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))

```

## 3.3. Tyro3_AT

```{r}
(Tyro3_A_T_plot <- ggplot(FC_thesis_m,
                        aes(x = A_N_cat, y = Tyro3_pg_ml, 
                            colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Tyro3 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,12000, by = 2000), limits = c(0,12000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = Tyro3_pg_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Tyro3_pg_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
            Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))
```

## 3.4. TREM2_AT

```{r}
(TREM2_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = TREM2_pg_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TREM2 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,14000, by = 2000), limits = c(0,14000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = TREM2_pg_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TREM2_pg_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
            Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))
```

## 3.5. C1q_AT

```{r}
(C1q_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = C1q_ng_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C1q (ng/mL)") +
    scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = C1q_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C1q_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
            Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))
```

## 3.6. C3_AT
```{r}
(C3_A_T_plot <- ggplot(FC_thesis_m,
                        aes(x = A_N_cat, y = C3_ng_ml, 
                            colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C3 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,20000, by = 5000), limits = c(0, 20000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = C3_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C3_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
            Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))
```


## 3.7. C4_AT
```{r}
(C4_A_T_plot <- ggplot(FC_thesis_m,
                       aes(x = A_N_cat, y = C4_ng_ml, 
                           colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C4 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,5000, by = 1000), limits = c(0, 5000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = C4_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C4_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
            Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))
```


## 3.8. FB_AT
```{r}
(FB_A_T_plot <- ggplot(FC_thesis_m,
                       aes(x = A_N_cat, y = Factor_B_ng_ml, 
                           colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor B (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = Factor_B_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_B_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
            Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))
```


## 3.9. FH_AT
```{r}
(FH_A_T_plot <- ggplot(FC_thesis_m,
                       aes(x = A_N_cat, y = Factor_H_ng_ml, 
                           colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor H (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = Factor_H_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_H_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
            Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))
```


## 3.10. MIF_AT
```{r}
(MIF_A_T_plot <- ggplot(FC_thesis_m,
                       aes(x = A_N_cat, y = MIF_pg_ml, 
                           colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "MIF (pg/mL)") +
    scale_y_continuous(breaks = seq(0,40000, by = 10000), limits = c(0, 40000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = MIF_pg_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = MIF_pg_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
            Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))
```


## 3.11. TNFR1_AT
```{r}
(TNFR1_A_T_plot <- ggplot(FC_thesis_m,
                        aes(x = A_N_cat, y = TNFR1_ng_ml, 
                            colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2.0, by = 0.5), limits = c(0, 2.0)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = TNFR1_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR1_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
            Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))
```


## 3.12. TNFR2_AT
```{r}
(TNFR2_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = TNFR2_ng_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR2 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,4, by = 1), limits = c(0, 4)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = TNFR2_ng_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR2_ng_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
            Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))
```


## 3.13. ICAM1_AT
```{r}
(ICAM1_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = ICAM1_ng_mL, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "ICAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,8, by = 2), limits = c(0, 8)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = ICAM1_ng_mL ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = ICAM1_ng_mL ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
            Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))
```


## 3.14. VCAM1_AT
```{r}
(VCAM1_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = VCAM1_ng_mL, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "VCAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,20, by = 5), limits = c(0, 20)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = VCAM1_ng_mL ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = VCAM1_ng_mL ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
            Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))
```


## 3.15. CRP_AT
```{r}
(CRP_A_T_plot <- ggplot(FC_thesis_m,
                          aes(x = A_N_cat, y = CRP_pg_ml, 
                              colour = A_N_cat, fill = A_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "CRP (pg/mL)") +
    scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
    scale_colour_manual(values = c("black","black", "black", "black")) +
    scale_fill_brewer(palette = "Accent") +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = CRP_pg_ml ~ A_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = CRP_pg_ml ~ A_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(A_N_cat)%>% 
  summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
            Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))
```


# 4. Diagnosis scheme
## 4.1. YKL40_Diag
```{r}
(YKL40_diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = YKL40_ng_ml, 
                              colour = Diag, fill = Diag)) +
   geom_boxplot() +
   geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
   labs(x = NULL, y = "YKL40 (ng/mL)") +
   scale_y_continuous(breaks = seq(0,1000, by = 250), limits = c(0,1000)) +
   scale_colour_manual(values = c("black", "black" )) + 
   scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
   theme_bw() +
   theme(legend.position = "none",
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank()))

ancova(formula = YKL40_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = YKL40_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
            Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))
```

## 4.2. Axl_Diag
```{r}
(AXL_diag_plot <- ggplot(FC_thesis_m,
                        aes(x = Diag, y = AXL_ng_ml, 
                            colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "AXL (ng/mL)") +
    scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = AXL_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = AXL_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
            Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))
```

## 4.3. Tyro3_Diag
```{r}
(Tyro3_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = Tyro3_pg_ml, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Tyro3 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,8000, by = 2000), limits = c(0,8000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = Tyro3_pg_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Tyro3_pg_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
            Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))
```

## 4.4. TREM2_Diag
```{r}
(TREM2_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = TREM2_pg_ml, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TREM2 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,15000, by = 3000), limits = c(0,15000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TREM2_pg_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TREM2_pg_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
            Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))
```

## 4.5. C1q_Diag
```{r}
(C1q_Diag_plot <- ggplot(FC_thesis_m,
                        aes(x = Diag, y = C1q_ng_ml, 
                            colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C1q (ng/mL)") +
    scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C1q_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C1q_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
            Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))
```

## 4.6. C3_Diag
```{r}
(C3_Diag_plot <- ggplot(FC_thesis_m,
                       aes(x = Diag, y = C3_ng_ml, 
                           colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C3 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,20000, by = 5000), limits = c(0, 20000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C3_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C3_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
            Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))
```

## 4.7. C4_Diag
```{r}
(C4_Diag_plot <- ggplot(FC_thesis_m,
                       aes(x = Diag, y = C4_ng_ml, 
                           colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C4 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,4000, by = 1000), limits = c(0, 4000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C4_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C4_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
            Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))
```

## 4.8. FB_Diag
```{r}
(FB_Diag_plot <- ggplot(FC_thesis_m,
                       aes(x = Diag, y = Factor_B_ng_ml, 
                           colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor B (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = Factor_B_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_B_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
            Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))

```

## 4.9. FH_Diag
```{r}
(FH_Diag_plot <- ggplot(FC_thesis_m,
                       aes(x = Diag, y = Factor_H_ng_ml, 
                           colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor H (ng/mL)") +
    scale_y_continuous(breaks = seq(0,1500, by = 500), limits = c(0, 1500)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))


ancova(formula = Factor_H_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_H_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
            Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))
```

## 4.10. MIF_Diag
```{r}
(MIF_Diag_plot <- ggplot(FC_thesis_m,
                        aes(x = Diag, y = MIF_pg_ml, 
                            colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "MIF (pg/mL)") +
    scale_y_continuous(breaks = seq(0,30000, by = 5000), limits = c(0, 30000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = MIF_pg_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = MIF_pg_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
            Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))
```

## 4.11. TNFR1_Diag
```{r}
(TNFR1_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = TNFR1_ng_ml, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,1.5, by = 0.5), limits = c(0, 1.5)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TNFR1_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR1_ng_ml ~ Diag,
       postHocCorr = "bonf")
FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
            Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))
```

## 4.12. TNFR2_Diag
```{r}
(TNFR2_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = TNFR2_ng_ml, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR2 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,3, by = 1), limits = c(0, 3)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TNFR2_ng_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR2_ng_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
            Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))
```

## 4.13. ICAM1_Diag
```{r}
(ICAM1_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = ICAM1_ng_mL, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "ICAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,6, by = 2), limits = c(0, 6)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = ICAM1_ng_mL ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = ICAM1_ng_mL ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
            Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))
```

## 4.14. VCAM1_Diag
```{r}
(VCAM1_Diag_plot <- ggplot(FC_thesis_m,
                          aes(x = Diag, y = VCAM1_ng_mL, 
                              colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "VCAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,15, by = 5), limits = c(0, 15)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = VCAM1_ng_mL ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = VCAM1_ng_mL ~ Diag,
       postHocCorr = "bonf")
FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
            Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))

```


## 4.15. CRP_Diag
```{r}
(CRP_Diag_plot <- ggplot(FC_thesis_m,
                        aes(x = Diag, y = CRP_pg_ml, 
                            colour = Diag, fill = Diag)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "CRP (pg/mL)") +
    scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
    scale_colour_manual(values = c("black", "black" )) + 
    scale_fill_manual(values = c("indianred1", "#1ca9c9")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = CRP_pg_ml ~ Diag + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = CRP_pg_ml ~ Diag,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(Diag)%>% 
  summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
            Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))
```



# 5. Diagnosis+T scheme
## 5.1. YKL40_Diag+T
```{r}
(YKL40_diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = YKL40_ng_ml, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
   geom_boxplot() +
   geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
   labs(x = NULL, y = "YKL40 (ng/mL)") +
   scale_y_continuous(breaks = seq(0,1000, by = 200), limits = c(0,800)) +
   scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
   scale_colour_manual(values = c("black",'black', "black", "black")) +
   scale_fill_manual(values = c("lightblue", "darkorange3", 
                                "lightgreen", "coral")) +
   theme_bw() +
   theme(legend.position = "none",
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank()))

ancova(formula = YKL40_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = YKL40_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(YKL40_ng_ml, na.rm = TRUE), Std=sd(YKL40_ng_ml, na.rm = TRUE),
            Max=max(YKL40_ng_ml, na.rm = TRUE), Min=min(YKL40_ng_ml, na.rm = TRUE))

```

## 5.2. Axl_Diag+T
```{r}
(AXL_diag_T_plot <- ggplot(FC_thesis_m,
                         aes(x = clinical_N_cat, y = AXL_ng_ml, 
                             colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "AXL (ng/mL)") +
    scale_y_continuous(breaks = seq(0,50, by = 10), limits = c(0,50)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = AXL_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = AXL_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")
FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(AXL_ng_ml, na.rm = TRUE), Std=sd(AXL_ng_ml, na.rm = TRUE),
            Max=max(AXL_ng_ml, na.rm = TRUE), Min=min(AXL_ng_ml, na.rm = TRUE))
```

## 5.3. Tyro3_Diag+T
```{r}
(Tyro3_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = Tyro3_pg_ml, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Tyro3 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,12000, by = 2000), limits = c(0,10000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = Tyro3_pg_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Tyro3_pg_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(Tyro3_pg_ml, na.rm = TRUE), Std=sd(Tyro3_pg_ml, na.rm = TRUE),
            Max=max(Tyro3_pg_ml, na.rm = TRUE), Min=min(Tyro3_pg_ml, na.rm = TRUE))
```

## 5.4. TREM2_Diag+T
```{r}
(TREM2_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = TREM2_pg_ml, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TREM2 (pg/mL)") +
    scale_y_continuous(breaks = seq(0,14000, by = 2000), limits = c(0,14000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TREM2_pg_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TREM2_pg_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(TREM2_pg_ml, na.rm = TRUE), Std=sd(TREM2_pg_ml, na.rm = TRUE),
            Max=max(TREM2_pg_ml, na.rm = TRUE), Min=min(TREM2_pg_ml, na.rm = TRUE))
```

## 5.5. C1q_Diag+T
```{r}
(C1q_Diag_T_plot <- ggplot(FC_thesis_m,
                         aes(x = clinical_N_cat, y = C1q_ng_ml, 
                             colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C1q (ng/mL)") +
    scale_y_continuous(breaks = seq(0,600, by = 100), limits = c(0,600)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C1q_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C1q_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(C1q_ng_ml, na.rm = TRUE), Std=sd(C1q_ng_ml, na.rm = TRUE),
            Max=max(C1q_ng_ml, na.rm = TRUE), Min=min(C1q_ng_ml, na.rm = TRUE))
```

## 5.6. C3_Diag+T
```{r}
(C3_Diag_T_plot <- ggplot(FC_thesis_m,
                        aes(x = clinical_N_cat, y = C3_ng_ml, 
                            colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C3 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,20000, by = 2000), limits = c(0, 20000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C3_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C3_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(C3_ng_ml, na.rm = TRUE), Std=sd(C3_ng_ml, na.rm = TRUE),
            Max=max(C3_ng_ml, na.rm = TRUE), Min=min(C3_ng_ml, na.rm = TRUE))

```

## 5.7. C4_Diag+T
```{r}
(C4_Diag_T_plot <- ggplot(FC_thesis_m,
                        aes(x = clinical_N_cat, y = C4_ng_ml, 
                            colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "C4 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,5000, by = 500), limits = c(0, 4000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = C4_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = C4_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(C4_ng_ml, na.rm = TRUE), Std=sd(C4_ng_ml, na.rm = TRUE),
            Max=max(C4_ng_ml, na.rm = TRUE), Min=min(C4_ng_ml, na.rm = TRUE))
```

## 5.8. FB_Diag+T
```{r}
(FB_Diag_T_plot <- ggplot(FC_thesis_m,
                        aes(x = clinical_N_cat, y = Factor_B_ng_ml, 
                            colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor B (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2500, by = 500), limits = c(0, 2000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = Factor_B_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_B_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(Factor_B_ng_ml, na.rm = TRUE), Std=sd(Factor_B_ng_ml, na.rm = TRUE),
            Max=max(Factor_B_ng_ml, na.rm = TRUE), Min=min(Factor_B_ng_ml, na.rm = TRUE))
```

## 5.9. FH_Diag+T
```{r}
(FH_Diag_T_plot <- ggplot(FC_thesis_m,
                        aes(x = clinical_N_cat, y = Factor_H_ng_ml, 
                            colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "Factor H (ng/mL)") +
    scale_y_continuous(breaks = seq(0,2000, by = 500), limits = c(0, 2000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))


ancova(formula = Factor_H_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = Factor_H_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(Factor_H_ng_ml, na.rm = TRUE), Std=sd(Factor_H_ng_ml, na.rm = TRUE),
            Max=max(Factor_H_ng_ml, na.rm = TRUE), Min=min(Factor_H_ng_ml, na.rm = TRUE))
```

## 5.10. MIF_Diag+T
```{r}
(MIF_Diag_T_plot <- ggplot(FC_thesis_m,
                         aes(x = clinical_N_cat, y = MIF_pg_ml, 
                             colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "MIF (pg/mL)") +
    scale_y_continuous(breaks = seq(0,30000, by = 5000), limits = c(0, 30000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = MIF_pg_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = MIF_pg_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(MIF_pg_ml, na.rm = TRUE), Std=sd(MIF_pg_ml, na.rm = TRUE),
            Max=max(MIF_pg_ml, na.rm = TRUE), Min=min(MIF_pg_ml, na.rm = TRUE))
```

## 5.11. TNFR1_Diag+T
```{r}
(TNFR1_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = TNFR1_ng_ml, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,1.5, by = 0.5), limits = c(0, 1.5)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TNFR1_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR1_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")
FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(TNFR1_ng_ml, na.rm = TRUE), Std=sd(TNFR1_ng_ml, na.rm = TRUE),
            Max=max(TNFR1_ng_ml, na.rm = TRUE), Min=min(TNFR1_ng_ml, na.rm = TRUE))

```

## 5.12. TNFR2_Diag+T
```{r}
(TNFR2_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = TNFR2_ng_ml, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "TNFR2 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,3, by = 1), limits = c(0, 3)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = TNFR2_ng_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = TNFR2_ng_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(TNFR2_ng_ml, na.rm = TRUE), Std=sd(TNFR2_ng_ml, na.rm = TRUE),
            Max=max(TNFR2_ng_ml, na.rm = TRUE), Min=min(TNFR2_ng_ml, na.rm = TRUE))
```


## 5.13. ICAM1_Diag+T
```{r}
(ICAM1_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = ICAM1_ng_mL, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "ICAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,7.5, by = 2.5), limits = c(0, 7.5)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = ICAM1_ng_mL ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = ICAM1_ng_mL ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(ICAM1_ng_mL, na.rm = TRUE), Std=sd(ICAM1_ng_mL, na.rm = TRUE),
            Max=max(ICAM1_ng_mL, na.rm = TRUE), Min=min(ICAM1_ng_mL, na.rm = TRUE))
```

## 5.14. VCAM1_Diag+T
```{r}
(VCAM1_Diag_T_plot <- ggplot(FC_thesis_m,
                           aes(x = clinical_N_cat, y = VCAM1_ng_mL, 
                               colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "VCAM1 (ng/mL)") +
    scale_y_continuous(breaks = seq(0,20, by = 5), limits = c(0, 20)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))
ancova(formula = VCAM1_ng_mL ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = VCAM1_ng_mL ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(VCAM1_ng_mL, na.rm = TRUE), Std=sd(VCAM1_ng_mL, na.rm = TRUE),
            Max=max(VCAM1_ng_mL, na.rm = TRUE), Min=min(VCAM1_ng_mL, na.rm = TRUE))
```

## 5.15. CRP_Diag+T
```{r}
(CRP_Diag_T_plot <- ggplot(FC_thesis_m,
                         aes(x = clinical_N_cat, y = CRP_pg_ml, 
                             colour = clinical_N_cat, fill = clinical_N_cat)) +
    geom_boxplot() +
    geom_jitter(pch = 18, size = 1, alpha = 0.8, width = 0.2) +
    labs(x = NULL, y = "CRP (pg/mL)") +
    scale_y_continuous(breaks = seq(0,50000, by = 12500), limits = c(0, 50000)) +
    scale_x_discrete(labels = c("SCD T-", "SCD T+", "MCI T-", "MCI T+")) +
    scale_colour_manual(values = c("black",'black', "black", "black")) +
    scale_fill_manual(values = c("lightblue", "darkorange3", 
                                 "lightgreen", "coral")) +
    theme_bw() +
    theme(legend.position = "none",
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank()))

ancova(formula = CRP_pg_ml ~ clinical_N_cat + 
         Age + 
         sex +
         bmi +
         E4_Positive,
       data = FC_thesis_m,
       postHoc = CRP_pg_ml ~ clinical_N_cat,
       postHocCorr = "bonf")

FC_thesis_m%>%
  group_by(clinical_N_cat)%>% 
  summarise(Median=median(CRP_pg_ml, na.rm = TRUE), Std=sd(CRP_pg_ml, na.rm = TRUE),
            Max=max(CRP_pg_ml, na.rm = TRUE), Min=min(CRP_pg_ml, na.rm = TRUE))
```




